Recently,many data anonymization methods have been proposed to protect privacy in the applications of data mining.But few of them have considered the threats from user's priori knowledge of data patterns.To solve ...Recently,many data anonymization methods have been proposed to protect privacy in the applications of data mining.But few of them have considered the threats from user's priori knowledge of data patterns.To solve this problem,a flexible method was proposed to randomize the dataset,so that the user could hardly obtain the sensitive data even knowing data relationships in advance.The method also achieves a high level of accuracy in the mining process as demonstrated in the experiments.展开更多
A children’s book as Alice’s Adventures in Wonderland,it is incredibly popular among adults.Finding out why the book arouses adults’interest will enhance people’s understanding of it.This paper draws on the elemen...A children’s book as Alice’s Adventures in Wonderland,it is incredibly popular among adults.Finding out why the book arouses adults’interest will enhance people’s understanding of it.This paper draws on the elements in the book which only adults could appreciate,and finds out that it requires A Priori knowledge,from mathematical analogies to logic,as well as A Posteriori knowledge,including the awareness of social hierarchy,to understand them,which leads to the book’s popularity among adults.展开更多
It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model...It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network （TSNN） is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10 8 , which makes neural network output more close to the simulated contaminant concentration.展开更多
文摘Recently,many data anonymization methods have been proposed to protect privacy in the applications of data mining.But few of them have considered the threats from user's priori knowledge of data patterns.To solve this problem,a flexible method was proposed to randomize the dataset,so that the user could hardly obtain the sensitive data even knowing data relationships in advance.The method also achieves a high level of accuracy in the mining process as demonstrated in the experiments.
文摘A children’s book as Alice’s Adventures in Wonderland,it is incredibly popular among adults.Finding out why the book arouses adults’interest will enhance people’s understanding of it.This paper draws on the elements in the book which only adults could appreciate,and finds out that it requires A Priori knowledge,from mathematical analogies to logic,as well as A Posteriori knowledge,including the awareness of social hierarchy,to understand them,which leads to the book’s popularity among adults.
基金Supported by the National Natural Science Foundation of China(51339004,71171151)
文摘It needs long time to predict radioactive contaminant diffusion in receiving water under accident condition by using computational fluid dynamics （CFD） model. In order to shorten the computation time, a hybrid model based on CFD and time series neural network （TSNN） is proposed in this paper. The concentration change of radioactive contamination in an inland reservoir after a postulated accident is studied as a case. The result shows that this hybrid model can predict the contaminant diffusion trend and shorten at least 50% of iteration time. Priori knowledge integrated into the neural network model is able to reduce the mean square error of network output to 9.66×10 8 , which makes neural network output more close to the simulated contaminant concentration.
